Principal Software Engineer

The Role You will be responsible for designing, developing, and overseeing the implementation of AI and machine learning solutions that align with Emerson Digital Grid Management business objectives. This role requires deep technical expertise in AI/ML frameworks, cloud infrastructure, and software architecture, as well as the ability to collaborate with cross-functional teams to deliver robust, scalable solutions. The ideal candidate is a strategic thinker with hands-on experience in building production-grade AI systems and a passion for advancing the field of artificial intelligence. Key Responsibilities: Architecture Design: Lead the design and development of scalable, reliable, and high-performance AI/ML architectures, including model pipelines, data processing systems, and inference systems. Solution Development: Collaborate with data scientists, software engineers, and product teams to translate business requirements into technical AI/ML solutions. Model Selection & Optimization: Evaluate and select appropriate machine learning models, frameworks, and algorithms to address specific use cases, ensuring optimal performance, scalability, and efficiency. Data Pipeline Development: Design and implement robust data pipelines for data ingestion, preprocessing, feature engineering, and model training. Cloud Integration: Architect AI/ML solutions on cloud platforms (e.g., AWS, Azure, GCP) to ensure scalability, cost-efficiency, and seamless deployment. Performance Monitoring: Define and implement metrics to monitor model performance, accuracy, and system reliability in production environments. Collaboration: Work closely with software engineers, DevOps, and product managers to integrate AI/ML models into production systems and ensure smooth operation. Research & Innovation: Stay abreast of the latest advancements in AI/ML technologies, tools, and methodologies, and incorporate cutting-edge techniques into our solutions. Mentorship: Provide technical guidance and mentorship to junior team members, fostering a culture of continuous learning and improvement. Compliance & Ethics: Ensure AI/ML solutions adhere to ethical guidelines, data privacy regulations, and industry standard Qualifications: Education: Bachelor’s or master’s degree in computer science, Data Science, Machine Learning, or a related field. Experience: 5+ years of experience in designing and implementing AI/ML systems in production environments. Proven track record of architecting scalable, cloud-based AI/ML solutions. Hands-on experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and deep learning techniques. Strong knowledge of cloud platforms (AWS, Azure, GCP) and their AI/ML services (e.g., SageMaker, Azure ML, Vertex AI). Experience with data pipeline tools (e.g., Apache Airflow, Kafka, Spark) and containerization (e.g., Docker, Kubernetes). Technical Skills: Proficiency in programming languages such as Python, R, or Java. Expertise in data modeling, feature engineering, and model evaluation techniques. Familiarity with MLOps practices, including CI/CD pipelines, model versioning, and monitoring. Strong understanding of distributed systems, microservices architecture, and API design. Soft Skills: Excellent problem-solving and analytical skills. Strong communication skills to articulate complex technical concepts to non-technical stakeholders. Ability to work collaboratively in a fast-paced, cross-functional team environment. Preferred Qualifications: Experience with generative AI, natural language processing (NLP), computer vision, or reinforcement learning. Knowledge of big data technologies (e.g., Hadoop, Snowflake, Databricks). Familiarity with Agile/Scrum methodologies. Contributions to open-source AI/ML projects or publications in relevant fields. Familiarity with Power Systems.